Investment managers have used a variety of theories, models and methods to guide their selection of investments in order to produce a desired level of return on an investment consistent with an expected degree of risk. This selection process is generally preceded by a process to select for a strategy for allocating an investment amount among various asset classes made from the available investment assets, and forms the subsequent process of selecting for the particular investments within each asset class with which to populate these strategies. Professional investment managers typically invest in categories of assets made from securities and including stocks, bonds and cash or cash equivalent investments such as short-term Treasury bills.
Beginning in the mid-1980's, one type of securities investment for which demand has grown enormously is mutual funds, and in particular, open-ended mutual funds. Mutual funds are “collective investment funds”, raising the capital through the sale of shares to collectively acquire and hold other securities. The share value of these mutual funds reflects the aggregate net-asset value (NAV) of the stocks held in the fund, as opposed to the market price of the fund's shares, as determined by investor demand, as is the case with shares of a closed-end mutual fund. Mutual funds are an example of a type of investments that can be termed “book-valued”, as opposed to market-valued. For book-valued collective investment funds, the expected return and risk is dependent on the expected future actions of the fund's management, and not on the vagaries of future market demand for their shares. For this reason, investment managers have long looked for ways to analyze the past performance of book-valued collective funds, relying on the persistence of fund manager performance to guide their selections of future fund investments to reliably produce a desired average level of returns, again, for an expected level of risk.
In the prior art, many attempts have been made to predict differences in future investment performance arising between funds within an asset class. Those that undertake such predictions use a standard system of performance measurement first introduced in of Modern Portfolio Theory (1952) to frame their efforts. In MPT, Dr. Harry Markowitz introduced the approach of evaluating investments in terms of a utility function that reflected both the expected return and the expected risk of an investment. He plotted past investment return (benefit) against the risk to the investment associated with that level of return (cost). This risk-return relationship is what is commonly referred to as ‘investment performance’ within the industry, and is denoted by the term ‘risk-adjusted returns’.
Dr. Markowitz also created the mathematical proof that the returns of an investment portfolio as a whole can be maximized for a given level of risk by combining investments that have dissimilar and offsetting patterns of returns variance, demonstrating that a portfolio's return variance is the product of the variances of each investment, plus the product of the pair-wise cross or co-variances between each investment. This approach of combining investments of dissimilar risk has become the primary strategy for allocating portfolio assets within the industry, and is the precursor asset allocation strategy process on which the investment selection process of this Application, as well as many others, rests.
The segmentation of the process of creating an investment portfolio into the processes of asset allocation strategy selection and subsequent investment selection process is driven by practical concerns. With a large population of available investments, computation of the pair-wise covariance between the returns of each investment option as required by asset allocation processes such as outlined in Markowitz' Modern Portfolio Theory have historically been a burdensome and slow process. As a result, managers have typically focused their asset allocation strategies on the combination of broad asset classes, deciding what allocation of available investment resources should be placed in each asset class. Under MPT, these asset classes are, by definition, groups of investments with uniquely similar patterns and levels of risk, and can be identified by one of several methods. The pattern and level of past returns of individual investments can be analyzed and grouped, or in the case of mutual funds, groups can be formed from categories of funds with similar stated investment objectives. Pair-wise co-variance analysis then becomes a simpler exercise, using a small number of broad asset classes as proxies for the funds contained within each class.
The prevailing academic theory regarding the distribution of the investments within an asset class in terms of their investment performance has assumed that such distribution is random. The population density of investments within an asset class, measured in terms of the joint probability function of risk and returns, is assumed to conform to a normal, symmetrical distribution. Professor William Sharpe popularized this belief in his 1964 thesis, Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk, and elsewhere, teaching that the relationship between the return for the investments within an asset class and the risk of these investments could be plotted in a two-dimensional risk-return space as a straight line that runs from the return on a zero-risk investment (typically taken as a U.S. Treasury Bill) through a rate of return associated with the average risk and return for the asset class or an associated market index. A deviation in an investment's performance from this ‘securities market’ or ‘equilibrium’ line has been characterized by Professor Sharpe and others as ‘random error’, the population of these random errors making up a normal distribution around the equilibrium line and summing to zero.
Professor Sharpe introduced an alternative measure of risk with his model, ‘beta’; a variable risk measure comprised of the covariance between an investment's periodic returns and those of the asset class average or associated benchmark. Using Professor Sharpe's risk measure, deviations in performance from an equilibrium line are an investment's ‘alpha’. Using Markowitz's absolute-risk measure, the deviation is an investments ‘differential return’.
Under either risk measure, the equilibrium line represents the asset-class average performance. Investments whose risk-adjusted returns lie above this line have a stronger than average performance. The steepness of this equilibrium line is reflective of the strength of the performance of the asset class as a whole. Prof. Sharpe's work has led to the practice of using the risk-adjusted return calculated from the slope of the equilibrium line of an asset class to be a proxy for the performance of individual investments within the class. Professors Markowitz and Sharpe received Nobel Prizes for their work in this area.
Prior to the last few years, consideration of investment selection theory and methods for predicting differences in future investment performance have been primarily academic. Overviews of this academic work appear in “On Persistence in Mutual Fund Performance”, Journal of Finance (March 1997) by Mark Carhart, and “New Facts in Finance”, Economic Perspective, Federal Reserve Bank of Chicago (1999) by John Cochrane. Much of this work has common roots in, and builds upon, the general theoretical framework of the work of Markowitz and Sharpe. The focus of this work has been, in general, either a study of (i) whether mutual funds can out-perform the general securities market, or (ii) whether the future performance of funds can be predicted from past performance. The second question is pertinent here. Processes to identify predictive differences have usually relied on methods to form groups of funds within an asset class population based on similar levels of past returns, risk or investment performance. While in general, earlier work following Markowitz and then Sharpe found no predictive value, later studies have shown short-term persistence in investment performance, but this persistence has been found to be short-lived, and only to work in certain market cycles. For example, if the analysis of past performance statistics produced a positive future alpha or differential return for a selected group, it might be minor and temporal, such as a 1% advantage lasting less than two years. Still later commentators have cited qualifying factors such as extraordinary market conditions and reversal of cycles, to explain away patterns that might otherwise be profitably exploited in an analysis of past performance.
In addition to academic study, the subject of investment portfolios and securities has been the subject of various patents, particularly in the last few years. Few of these patents deal with the issues of asset allocation among classes or the selection of investments within an asset class. Fewer still attempt to predict future performance on any basis. One general approach of the patents dealing with predictive processes in regards to asset allocation and investment selection is to analyze external factors (as opposed to past performance and other factors intrinsic to the investments under consideration) to select investments or asset allocation strategies. External factors include “external economic factors”, “anticipated changes in economic factors”, “market conditions”, “target and failback scenarios”, and “predicted market performance”. U.S. Pat. Nos. 5,774,881; 5,884,287; 5,987,433; 6,018,722; 6,021,397; 6,078,904; and 6,125,355 are exemplary.
Another general approach in the prior art is to select asset allocation strategies or make investment selections based on differences in future performance as predicted by a third-party or as anticipated by assuming the ongoing persistence of historical performance. U.S. Pat. No. 5,806,049 uses external existing information on the characteristics of the investments, e.g. current ownership of assets, and anticipated changes in the quantity available. U.S. Pat. No. 6,003,018 assumes an external source of forecasts, e.g. a defined expected return and a defined standard deviation of return for each asset. U.S. Pat. No. 5,784,696 assumes that future returns and risks will equal historical returns and risks for each alternative. Likewise, U.S. Pat. No. 5,109,475 describes a predictive neural network that relies on the assumption that future risk and return for a stock will equal past risk and return. U.S. Pat. Nos. 5,761,442 and 5,978,778 attempt to predict future stock or security performance exclusively on the differences in individual performance values such as strong price momentum, historical price and volume, earning, cash flow, etc.
None of these known theories, models or techniques has found acceptance as a reliable method for looking at past investment performance to predict future investment performance over an extended period, namely, more than a year. Nor have any of the known theories, models or techniques proven to be reliably strongly superior in predictive power or reliable irrespective of market cycles.
It is therefore a principal object of this invention to provide a method of selecting for investments within an asset class of book-valued assets that does reliably predict differences in the future performance of selected investments within that class for at least 36 months.
Another principal object is to provide a method with the foregoing advantages that is strongly predictive, and is reliable regardless of market conditions, and produces a magnitude of superior future performance that justifies the cost of practicing the selection process.